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Improved multi-objective clustering algorithm using particle swarm optimization.

ABSTRACT: Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

SUBMITTER: Gong C

PROVIDER: S-EPMC5716574 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Improved multi-objective clustering algorithm using particle swarm optimization.

Gong Congcong C Chen Haisong H He Weixiong W Zhang Zhanliang Z

PloS one 20171205 12

Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strat ...[more]

PMID: 29206880

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